Added window for Spindlw

Author: HandsomeYun

.gitignore

@@ -1,5 +1,5 @@
-img
-__pycache__
-.DS_Store
-build
+img
+__pycache__
+.DS_Store
+build
 dist   

.idea/.gitignore

@@ -1,8 +1,8 @@
-Academic Software License: © 2023 UCLA (“Institution”).  Academic or nonprofit researchers are permitted to use this Software (as defined below) subject to Paragraphs 1-4:
-
-1. Institution hereby grants to you free of charge, so long as you are an academic or nonprofit researcher, a nonexclusive license under Institution’s copyright ownership interest in this software and any derivative works made by you thereof (collectively, the “Software”) to use, copy, and make derivative works of the Software solely for educational or academic research purposes, in all cases subject to the terms of this Academic Software License. Except as granted herein, all rights are reserved by Institution, including the right to pursue patent protection of the Software.
-2. Please note you are prohibited from further transferring the Software -- including any derivatives you make thereof -- to any person or entity. Failure by you to adhere to the requirements in Paragraphs 1 and 2 will result in immediate termination of the license granted to you pursuant to this Academic Software License effective as of the date you first used the Software.
-3. IN NO EVENT SHALL INSTITUTION BE LIABLE TO ANY ENTITY OR PERSON FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE USE OF THIS SOFTWARE, EVEN IF INSTITUTION HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. INSTITUTION SPECIFICALLY DISCLAIMS ANY AND ALL WARRANTIES, EXPRESS AND IMPLIED, INCLUDING, BUT NOT LIMITED TO, ANY IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE IS PROVIDED “AS IS.” INSTITUTION HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS OF THIS SOFTWARE.
-4. Any academic or scholarly publication arising from the use of this Software or any derivative works thereof will include the following acknowledgment:  The Software used in this research was created by Yipeng Zhang, Xin Chen, Hoyoung Chung, Lawrence Liu, Yuanyi Ding, Hiroki Nariai, Vwani Roychowdhury of UCLA. © 2023 UCLA.
-
+Academic Software License: © 2023 UCLA (“Institution”).  Academic or nonprofit researchers are permitted to use this Software (as defined below) subject to Paragraphs 1-4:
+
+1. Institution hereby grants to you free of charge, so long as you are an academic or nonprofit researcher, a nonexclusive license under Institution’s copyright ownership interest in this software and any derivative works made by you thereof (collectively, the “Software”) to use, copy, and make derivative works of the Software solely for educational or academic research purposes, in all cases subject to the terms of this Academic Software License. Except as granted herein, all rights are reserved by Institution, including the right to pursue patent protection of the Software.
+2. Please note you are prohibited from further transferring the Software -- including any derivatives you make thereof -- to any person or entity. Failure by you to adhere to the requirements in Paragraphs 1 and 2 will result in immediate termination of the license granted to you pursuant to this Academic Software License effective as of the date you first used the Software.
+3. IN NO EVENT SHALL INSTITUTION BE LIABLE TO ANY ENTITY OR PERSON FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE USE OF THIS SOFTWARE, EVEN IF INSTITUTION HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. INSTITUTION SPECIFICALLY DISCLAIMS ANY AND ALL WARRANTIES, EXPRESS AND IMPLIED, INCLUDING, BUT NOT LIMITED TO, ANY IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE IS PROVIDED “AS IS.” INSTITUTION HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS OF THIS SOFTWARE.
+4. Any academic or scholarly publication arising from the use of this Software or any derivative works thereof will include the following acknowledgment:  The Software used in this research was created by Yipeng Zhang, Xin Chen, Hoyoung Chung, Lawrence Liu, Yuanyi Ding, Hiroki Nariai, Vwani Roychowdhury of UCLA. © 2023 UCLA.
+
 Commercial entities: please contact zyp5511@gmail.com or software@tdg.ucla.edu for licensing opportunities.

.idea/inspectionProfiles/Project_Default.xml

@@ -1,80 +1,80 @@
-# PyHFO
-[ProjectPage](https://roychowdhuryresearch.github.io/PyHFO_Project_Page/) |
-[Download](https://github.com/roychowdhuryresearch/pyHFO/releases) | 
-[Manual](https://docs.google.com/document/d/1KzQpfuPFDk2lr9V3TgSkmc21jISB54ZOxo3pIYKQsp0/edit?usp=sharing) |
-
-PyHFO is a multi-window desktop application providing an integrated and user-friendly platform that includes time-efficient HFO detection algorithms such as short-term energy (STE) and Montreal Neurological Institute and Hospital (MNI) detectors and deep learning models for artifact and HFO with spike classification.
-
-## Bibtex
-If you find our project is useful in your research, please cite:
-
-```
-Zhang, Y., Liu, L., Ding, Y., Chen, X., Monsoor, T., Daida, A., Oana, S., Hussain, S. A., Sankar, R., Fallah, A., Santana-Gomez, C., Engel, J., Staba, R. J., Speier, W., Zhang, J., Nariai, H., & Roychowdhury, V. (2024). PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application. Journal of neural engineering, 10.1088/1741-2552/ad4916. Advance online publication. https://doi.org/10.1088/1741-2552/ad4916
-```
-
-## Related Projects
-
-* [HFODetector](https://github.com/roychowdhuryresearch/HFO_Detector) - A Python toolbox for very fast HFO detection.
-
-* [HFO-Classification](https://github.com/roychowdhuryresearch/HFO-Classification) - Many HFO classification projects powered by deep learning.
-
-* [EEG-Viz](https://github.com/jebbica/EEG-Viz) - A Python toolbox for EEG visualization.
-
-
-## Installation
-
-You can download the latest version of PyHFO from the [releases](https://github.com/roychowdhuryresearch/pyHFO/releases) page.
-
-If you choose to use the **macOS version** of the standalone distributable application, please follow these additional steps:
-
-1. **Download and unzip** the `.zip` file.
-2. You will get a file named `pyHFO.dmg`.
-3. Navigate to the directory containing the `pyHFO.dmg` file.
-4. Open the terminal and run the following command to remove the quarantine attribute:
-
-```
-xattr -cr pyHFO.dmg
-```
-
-You can also install it from the source code:
-
-```
-git clone https://github.com/roychowdhuryresearch/pyHFO.git 
-cd pyHFO
-pip install -r requirements.txt
-python main.py
-```
-
-## Usage
-
-The overview of the PyHFO is shown below:
-![Alt text](img/overview.png)
-
-
-The manual is available [here](https://docs.google.com/document/d/1KzQpfuPFDk2lr9V3TgSkmc21jISB54ZOxo3pIYKQsp0/edit?usp=sharing).
-
-## License
-
-This project is licensed under the UCLA Academic License - see the [LICENSE](LICENSE) file for details.
-
-## Acknowledgments
-
-### Contributors:
-This project is under supervsion of Prof. [Vwani Roychowdhury](https://www.ee.ucla.edu/vwani-p-roychowdhury/).
-
-Department of Electrical and Computer Engineering, University of California, Los Angeles
-- [Yipeng Zhang](https://zyp5511.github.io/)
-- [Lawrence Liu](https://www.linkedin.com/in/lawrence-liu-0a01391a7/)
-- [Yuanyi Ding](https://www.linkedin.com/in/yuanyi-ding-4a981a132/)
-- [Xin Chen](https://www.linkedin.com/in/xin-chen-980521/)
-- [Jessica Lin](https://www.linkedin.com/in/jessica4903/)
-
-Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital David Geffen School of Medicine
-- [Hiroki Nariai](https://www.uclahealth.org/providers/hiroki-nariai)
-
-
-
-
-
-
-
+# PyHFO
+[ProjectPage](https://roychowdhuryresearch.github.io/PyHFO_Project_Page/) |
+[Download](https://github.com/roychowdhuryresearch/pyHFO/releases) | 
+[Manual](https://docs.google.com/document/d/1KzQpfuPFDk2lr9V3TgSkmc21jISB54ZOxo3pIYKQsp0/edit?usp=sharing) |
+
+PyHFO is a multi-window desktop application providing an integrated and user-friendly platform that includes time-efficient HFO detection algorithms such as short-term energy (STE) and Montreal Neurological Institute and Hospital (MNI) detectors and deep learning models for artifact and HFO with spike classification.
+
+## Bibtex
+If you find our project is useful in your research, please cite:
+
+```
+Zhang, Y., Liu, L., Ding, Y., Chen, X., Monsoor, T., Daida, A., Oana, S., Hussain, S. A., Sankar, R., Fallah, A., Santana-Gomez, C., Engel, J., Staba, R. J., Speier, W., Zhang, J., Nariai, H., & Roychowdhury, V. (2024). PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application. Journal of neural engineering, 10.1088/1741-2552/ad4916. Advance online publication. https://doi.org/10.1088/1741-2552/ad4916
+```
+
+## Related Projects
+
+* [HFODetector](https://github.com/roychowdhuryresearch/HFO_Detector) - A Python toolbox for very fast HFO detection.
+
+* [HFO-Classification](https://github.com/roychowdhuryresearch/HFO-Classification) - Many HFO classification projects powered by deep learning.
+
+* [EEG-Viz](https://github.com/jebbica/EEG-Viz) - A Python toolbox for EEG visualization.
+
+
+## Installation
+
+You can download the latest version of PyHFO from the [releases](https://github.com/roychowdhuryresearch/pyHFO/releases) page.
+
+If you choose to use the **macOS version** of the standalone distributable application, please follow these additional steps:
+
+1. **Download and unzip** the `.zip` file.
+2. You will get a file named `pyHFO.dmg`.
+3. Navigate to the directory containing the `pyHFO.dmg` file.
+4. Open the terminal and run the following command to remove the quarantine attribute:
+
+```
+xattr -cr pyHFO.dmg
+```
+
+You can also install it from the source code:
+
+```
+git clone https://github.com/roychowdhuryresearch/pyHFO.git 
+cd pyHFO
+pip install -r requirements.txt
+python main.py
+```
+
+## Usage
+
+The overview of the PyHFO is shown below:
+![Alt text](img/overview.png)
+
+
+The manual is available [here](https://docs.google.com/document/d/1KzQpfuPFDk2lr9V3TgSkmc21jISB54ZOxo3pIYKQsp0/edit?usp=sharing).
+
+## License
+
+This project is licensed under the UCLA Academic License - see the [LICENSE](LICENSE) file for details.
+
+## Acknowledgments
+
+### Contributors:
+This project is under supervsion of Prof. [Vwani Roychowdhury](https://www.ee.ucla.edu/vwani-p-roychowdhury/).
+
+Department of Electrical and Computer Engineering, University of California, Los Angeles
+- [Yipeng Zhang](https://zyp5511.github.io/)
+- [Lawrence Liu](https://www.linkedin.com/in/lawrence-liu-0a01391a7/)
+- [Yuanyi Ding](https://www.linkedin.com/in/yuanyi-ding-4a981a132/)
+- [Xin Chen](https://www.linkedin.com/in/xin-chen-980521/)
+- [Jessica Lin](https://www.linkedin.com/in/jessica4903/)
+
+Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital David Geffen School of Medicine
+- [Hiroki Nariai](https://www.uclahealth.org/providers/hiroki-nariai)
+
+
+
+
+
+
+